Improving quality control for in‐clinic hematology analyzers: Common myths and opportunities

A robust quality management system for automated hematology an alyzers is crucial for generating high- quality results and instilling con fidence in analyzer function. A comprehensive quality management system comprises multiple aspects. Two major aspects are quality assurance (QA) and quality control (QC). QA is a framework and strategy for ensuring quality across the entire process, from blood draw through reported results. QA thus encompasses preanalytical to postanalytical variables related to result generation. QC, on the other hand, is focused exclusively on the analytical portion of quality management to ensure the function and performance of the analyzer itself. QC facilitates identification of analyzer problems so they can be addressed and fixed in a timely fashion. Recommendations for QC, including QC materials (QCMs), frequency of QC, and interpre tation of QC results, are specific for the analyzer. The ideal QCM would (1) be able to assess the function and per formance of all aspects of the hematology analyzer, (2) have a long shelf life, (3) be easy for the user to run and interpret, and (4) instill confidence in results for all species tested. To understand these re -quirements, it is imperative to understand the workflow of an au tomated hematology analyzer. Although the specifics vary among analyzers, the workflow follows the same general pattern (Table 1). The recommended QCM and the algorithms to interpret the QC results vary among analyzers to differences in chemistry and detection methods. In to basic analyzer function, QC for species- specific differ ences in performance, bias

A robust quality management system for automated hematology analyzers is crucial for generating high-quality results and instilling confidence in analyzer function. A comprehensive quality management system comprises multiple aspects. Two major aspects are quality assurance (QA) and quality control (QC). QA is a framework and strategy for ensuring quality across the entire process, from blood draw through reported results. QA thus encompasses preanalytical to postanalytical variables related to result generation. QC, on the other hand, is focused exclusively on the analytical portion of quality management to ensure the function and performance of the analyzer itself. QC facilitates identification of analyzer problems so they can be addressed and fixed in a timely fashion. Recommendations for QC, including QC materials (QCMs), frequency of QC, and interpretation of QC results, are specific for the analyzer.
The ideal QCM would (1) be able to assess the function and performance of all aspects of the hematology analyzer, (2) have a long shelf life, (3) be easy for the user to run and interpret, and (4) instill confidence in results for all species tested. To understand these requirements, it is imperative to understand the workflow of an automated hematology analyzer. Although the specifics vary among analyzers, the workflow follows the same general pattern ( Table 1).
The recommended QCM and the algorithms to interpret the QC results vary among analyzers due to differences in chemistry and detection methods. In addition to basic analyzer function, QC for veterinary analyzers should be able to detect species-specific differences in performance, including bias and drift.
For analyzers in veterinary practices, ease of performing and interpreting QC is particularly important. Unlike reference or academic laboratories, staff in clinics typically do not have extensive training in quality management and may not have the same commitment to following QC recommendations and troubleshooting issues. 1 Recommendations for in-clinic analyzers are often similar to those for reference laboratory instrumentation but with less frequent QC analysis and simpler rules for interpretation. For example, manufacturer-recommended QC frequency for in-clinic analyzers is often once per month versus daily.
Regulations mandating QA and QC for veterinary in-clinic analyzers vary regionally. Nevertheless, one study of in-clinic analyzers in human medical settings, where stringent mandatory regulations exist, 2 found that 19% of operators had not been trained to use the analyzer, 25% of operators failed to follow the manufacturer's procedures, and 32% failed to perform QC. 3 The results of such a study would likely be similar or worse if done in veterinary practices. Practitioners often use in-clinic analyzers to streamline patient care with rapid results that can immediately inform patient care, and they might not understand the importance of QC for instilling confidence in those results or the risks of not performing QC.
Hematology QC is often misunderstood by general practitioners, clinical pathologists, and other veterinary specialists. There are several myths surrounding QC that need clarification for better evaluation of the true benefits and shortcomings of traditional QC and QCM. In this editorial, we will address some common myths about QCM functionality and opportunities to continue to improve QC for in-clinic hematology analyzers through automation and inclusion of patient samples.     5,6 In reference or academic laboratories, strategies like Six Sigma can be used to evaluate these risks and determine the optimal frequency of QC, the number of samples that can be analyzed between QC analysis, and the optimal rules for the interpretation of QC results [7][8][9] ; however, many of these methods are impractical for use with in-clinic analyzers in the general practice setting. Although the same types of analyzer problems can occur in the reference or academic laboratory and the in-clinic laboratory setting, there may be differences in the quality goals, number of samples at risk, potential mitigating factors, and potential cost of mitigation.
Manufacturers of in-clinic hematology analyzers may recommend a minimum of weekly or monthly external QC instead of daily. These are meant as minimum recommendations that would be appropriate for clinics that analyze a moderate number of samples; however, they might not be optimal for clinics at the extremes who either analyze large numbers of samples or rarely use the analyzer. In many clinics, the ratio of FC-QCM to patient sample analyses will be higher than in reference laboratories, even when minimum recommendations for QC are followed. If daily external QC is performed, this ratio would be further increased to a point that the time and financial burden could inadvertently discourage QC compliance for clinics. QC recommendations for in-clinic analyzers would ideally incorporate more specific   QCMs, like FC-QCMs, do not directly mimic patient sample cells and require QCM-specific algorithms and workflow steps ( Table 2). This automation can also relieve some of the burden of QC from busy clinics.

OPP ORTUNITIE S FOR IMPROVING THE FUTURE OF QC
There are opportunities to continually improve the QC experience and compliance for in-clinic analyzers by extending QCM shelf life, improving thermal stability of QCM, and automating many of the QC activities. The concepts described here can be adapted and applied to different hematology analyzer technologies and are not specific to any particular manufacturer. Hematology analyzer manufacturers should strive toward improving QC in ways that increase the quality of results from in-clinic analyzers, reduce the financial and logistical burdens of QC, and improve overall patient care by minimizing analytical errors.